The areas of analysis, development, and fabrication of micro-entities have become widespread in various technologies in recent years. Micro-entities, as used herein, may include cells, microorganisms, micro- or nano-particles, droplets, molecules, proteins, peptides, calibration particles, and microfabricated structures such as microelectromechanical (MEM) structures, microelectronic chips, and microsensors. A micro-entity may include any entity (man-made or naturally occurring) that has a maximum dimension less than about 1 millimeter (mm). In some research, medical, or fabrication applications, micro-entities may be moved in a stream in large numbers. The stream may be part of a system that controls the movement and the metering of micro-entities into the stream. One example application is the characterization and sorting of cells in a flow cytometer. Another example may be movement, observation, and sorting of micro-entities in microfluidic channels.
In some applications, it may be desirable to know the locations precisely of a selected micro-entity within the moving stream at one or more selected times, or in other embodiments, to know the times precisely when a selected micro-entity will arrive at one or more locations. For example and referring to FIG. 1, a micro-entity 125 may move in a stream 120 of micro-entities. A stream 120 of micro-entities may comprise a fluid stream (e.g., a gas, liquid, or particulate stream) or flow path capable of conveying the micro-entities. The micro-entities may be in suspension in the stream. In some cases, a stream 120 of micro-entities may comprise a dense collection of the micro-entities that moves in a stream-like manner. A stream 120 of micro-entities may be conveyed by any combination of mechanical, electrical, and magnetic means.
A micro-entity 125 may be detected at a first location P1 at a first time t1, and it may be desirable to know at what time t2 the micro-entity 125 will arrive at a pre-selected second location P2 or a plurality of other locations along the stream. Conversely, the micro-entity 125 may be detected at the first location P1 at a first time t1, and it may be desirable to know at what location P3, or locations, the micro-entity 125 will be after a pre-selected elapsed time t3, or times. In the former case, predicting the arrival time t2 can be useful in determining when a measurement or an operation (e.g., a sorting operation, an imaging operation, a charging operation, a transforming operation, an illumination of the entity, a signal detection etc.) may be performed on the micro-entity at the second location P2. In the latter case, predicting the location P3 of the micro-entity 125 may be useful for precisely tracking the movement and/or evolution of the entity in the stream, e.g., acquiring and overlaying multiple images of the micro-entity as it moves along the stream.
One method for predicting the arrival time t2 or location P3 of a micro-entity in a moving stream is to calculate an expected value of t2 or P3 based upon an average stream velocity vavg. The average stream velocity may be measured or determined in any suitable way. If vavg is found or known, then either t2 or P3 may be determined through the relation d=vavg×t, where d is the distance traveled by the micro-entity 125 in an elapsed time t. An alternative method for predicting an arrival time t2 at a predetermined location P2 is to add an average transit time or delay time Δtavg to the value of t1 after observing the entity at P1. The time Δtavg may be measured or determined in any suitable way. In some cases, these methods of predicting arrival times or locations of moving micro-entities may be sufficient.
Flow cytometers are examples of systems which utilize streams to transport micro-entities for purposes of characterizing and sorting the micro-entities, such as biological cells. Flow cytometers are used widely for rapidly analyzing heterogeneous cell suspensions to identify constituent sub-populations. Examples of the many applications where flow cytometry cell sorting is finding use include isolation of rare populations of immune system cells for AIDS research, isolation of genetically atypical cells for cancer research, isolation of specific chromosomes for genetic studies, and isolation of various species of microorganisms for environmental studies. For example, fluorescently labeled monoclonal antibodies are often used as “markers” to identify immune cells such as T lymphocytes and B lymphocytes, clinical laboratories use this technology to count the number of “CD4 positive” T lymphocytes in HIV infected patients, and they also use this technology to identify cells associated with a variety of leukemia and lymphoma cancers.
Recently, two areas of interest are moving cell sorting towards clinical, patient-care applications, rather than strictly research applications. First is the move away from chemical pharmaceutical development to the development of biopharmaceuticals. For example, many new cancer therapies utilize biological material. These include a class of antibody-based cancer therapeutics. Cell sorters can play an important role in the identification, development, purification and, ultimately, production of these products.
Related to this is a move toward the use of cell replacement therapy for patient care. Much of the current interest in stem cells revolves around a new area of medicine often referred to as regenerative therapy or regenerative medicine. These therapies may often require that relatively rare cells be isolated from patient tissue. For example, adult stem cells may be isolated from bone marrow and ultimately used as part of a re-infusion back into the patient from whom they were removed. Flow cytometry and cell sorting are important tissue processing tools that may enable delivery of such therapies.